EXPLORING MODELS OF CARE WITH A TEXT MESSAGE-BASED INTERVENTION FOR ADOLESCENTS AND YOUNG ADULTS WITH BENIGN HEMATOLOGICAL DISEASE
Bibliographic record
Abstract
BACKGROUND: Adolescents and young adults (AYA) with chronic diseases transitioning from pediatric care to adult care have unique needs that are often neglected. As AYA learn to manage their schedules, the most commonly reported reason for missed appointments was forgetfulness. Studies have shown using short message service (SMS) to remind patients of clinic appointments can reduce missed appointments and improve young patients’ engagement in the management of their chronic disease. OBJECTIVES: We sought to integrate mobile technology into an AYA transitional care clinic via SMS reminders to notify patients of scheduled appointments, and measure the impact of this technology on compliance and no-show rates in the clinic. A secondary objective was to identify the characteristics and needs of a cohort of AYA patients. DESIGN/METHODS: We conducted a pre- and post- intervention study to compare monthly no-show rates in 18-25 year olds at an AYA benign hematology clinic. All patients enrolled in this clinic from September 2015 to present have been eligible for this study. Patients were consented at the time of appointment booking, and received SMS reminders and a link to an online study survey 3 business days prior to their appointment. We compared monthly pre-intervention no-show rates to post-intervention rates using a Chi-square test with significant p-value of <0.05. RESULTS: We have recruited 68 AYA patients and sent out 102 SMS in the post-intervention phase to date. SMS reduced missed appointments from 31.35% (91/290) to 16.2% (15/93), however, the results were not statistically significant (p=0.808). Among patients who missed appointments, 4 patients were repeat no-shows, and 5 patients have a history of no-shows from pediatric care. Five patients rescheduled appointments, and 4 who were unable to attend for medical reasons were not counted as missed appointments. Through the survey, patients agreed SMS reminders were helpful; additional patient characteristics and related data are being analyzed. All patients consented to participate; however 4 did not provide a mobile number and were excluded from the study. Recruitment is on-going. CONCLUSION: Preliminary results show SMS reminders may be an effective intervention to improve clinic attendance at an AYA hematology clinic. The SMS reminders have been well received, and have presumably impacted care in a positive way. A future study could assess the integration of such technology at a pediatric clinic to empower teenagers (ages 13-18) to engage in the management of their own healthcare treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".